Inferring the QoE of HTTP video streaming from user-viewing activities

Mok, Ricky K. P.; Chan, Ehw; Wang, Liu; Chang, Rocky K. C.

doi:10.1145/2018602.2018611

Cited by 142 publications

(70 citation statements)

References 22 publications

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“…Although past measurement studies (e.g., [10,15,20,22,32,33,40,41]) have also presented some of these observations in a different context, our contribution lies in synthesizing these observations in the context of Internet video QoE.…”

Section: Challenges In Developing Video Qoementioning

confidence: 99%

“…User studies: Prior work by the multimedia community try to assess video quality by performing subjective user studies and validating objective video quality models against the user study scores [11,18,25,30,32]. User studies are typically done at a small-scale with a few hundred users and the perceptual scores given by users under a controlled setting may not translate into measures of user engagement in the wild.…”

Section: Related Workmentioning

confidence: 99%

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Developing a predictive model of quality of experience for internet video

BalachandranAthula

SekarVyas

AkellaAditya

et al. 2013

SIGCOMM Comput. Commun. Rev.

184

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Improving users' quality of experience (QoE) is crucial for sustaining the advertisement and subscription based revenue models that enable the growth of Internet video. Despite the rich literature on video and QoE measurement, our understanding of Internet video QoE is limited because of the shift from traditional methods of measuring video quality (e.g., Peak Signal-to-Noise Ratio) and user experience (e.g., opinion scores). These have been replaced by new quality metrics (e.g., rate of buffering, bitrate) and new engagement-centric measures of user experience (e.g., viewing time and number of visits). The goal of this paper is to develop a predictive model of Internet video QoE. To this end, we identify two key requirements for the QoE model: (1) it has to be tied in to observable user engagement and (2) it should be actionable to guide practical system design decisions. Achieving this goal is challenging because the quality metrics are interdependent, they have complex and counter-intuitive relationships to engagement measures, and there are many external factors that confound the relationship between quality and engagement (e.g., type of video, user connectivity). To address these challenges, we present a data-driven approach to model the metric interdependencies and their complex relationships to engagement, and propose a systematic framework to identify and account for the confounding factors. We show that a delivery infrastructure that uses our proposed model to choose CDN and bitrates can achieve more than 20% improvement in overall user engagement compared to strawman approaches.

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Section: Challenges In Developing Video Qoementioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Developing a predictive model of quality of experience for internet video

BalachandranAthula

SekarVyas

AkellaAditya

et al. 2013

SIGCOMM Comput. Commun. Rev.

184

View full text Add to dashboard Cite

show abstract

“…The problem of QoE assessment in HTTP video streaming is already well-known and well studied, and different QoE models for video streaming have been proposed in the past [7], [10], [12], [13], [15], [21], [23]- [25]. Today it is well accepted that stalling (i.e., stops of the video playback) and initial delay on the video playback are the most relevant KPIs for video streaming QoE [12]- [14], [23].…”

Section: Related Workmentioning

confidence: 99%

“…There are different tools [5], [6], [19] which are capable of monitoring application-layer metrics which are highly correlated to QoE in video streaming services. Buffering events or stallings, video quality/resolution switches and initial playback delay are accepted today as the key application-layer metrics which can be used to predict the QoE undergone by the video watcher, using different models proposed and investigated in the literature [7], [10], [12], [13], [15], [21], [23]- [25]. Out of these metrics, stalling is the paramount one, specially when it comes to mobile video watched in small end-devices such as smartphones; in fact, in [11] we show that QoE for video streaming in modern smartphones is actually slightly impaired by video resolution changes.…”

Section: Introductionmentioning

confidence: 99%

Improving QoE prediction in mobile video through machine learning

Casas

Wassermann

2017

2017 8th International Conference on the Network of the Future (NOF)

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Abstract-Despite the massive adoption of HTTP adaptive streaming technology, buffering is still the most harmful event for QoE in video streaming. Previous studies have shown that buffering is not only detrimental for the overall user experience, but is also highly correlated to viewer engagement. The occurrence of buffering is particularly critical in cellular networks and mobile video deployments, as network conditions are less stable and network resources more limited. In this context, monitoring and properly predicting the QoE of video streaming services becomes paramount to cellular network operators, who need to offer high quality levels to reduce the risks of customers churning for quality dissatisfaction. In this paper, we present a novel approach to multi-dimensional QoE prediction in mobile video using machine learning models. Contrary to previous models for QoE prediction in video streaming, which are generally uni-or low-dimensional and model the impact of single video descriptors independently, we use a high-dimensional input space to model the impact of buffering and initial delay on QoE. We train and test the proposed models on a publicly available mobile video dataset, generated from subjective QoE tests with real viewers. Besides improving prediction performance, the proposed models show that there is a clear influence of other buffering pattern descriptors generally neglected in previous models -in particular those linked to the occurrence of the last stalling event, shedding light on new KPIs to monitor for better QoE assessment in video streaming.

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“…Most of these studies focus on the effect of bitrate switching on users' subjective quality and do not consider the effect of seeking operation. Mok et al have assessed the relationship between user-viewing activities and QoE [8]. The userviewing activities are pause, resume, refresh, forward time shift, and switch to a higher video quality among others.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Seeking Operation on QoE of HTTP Adaptive Streaming Services

Nunome¹,

Tani²

2017

IJCNC

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In this paper, we assess multidimensional QoE (Quality of Experience) of HTTP-based streaming services in seeking operation to evaluate the effect of two transmission schemes: adaptive bitrate streaming and progressive download. We perform a subjective experiment with two contents and various network load conditions. In the experiment, subjects find pre-specified scenes by means of seeking the video as they want to see the scenes right now. We also perform the principal component analysis for the assessment result of multidimensional QoE. We then find that the adaptive bitrate streaming is not necessarily effective for QoE enhancement; the effectiveness of the scheme depends on the usage of the system and network conditions.

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Inferring the QoE of HTTP video streaming from user-viewing activities

Cited by 142 publications

References 22 publications

Developing a predictive model of quality of experience for internet video

Developing a predictive model of quality of experience for internet video

Improving QoE prediction in mobile video through machine learning

The Effect of Seeking Operation on QoE of HTTP Adaptive Streaming Services

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